Artificial Intelligence (AI) is transforming our digital landscape, but it can be overwhelming for newcomers. This blog post simplifies key AI terminology to help you navigate this exciting field confidently.
Artificial Intelligence (AI)
Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.
Machine Learning (ML)
Machine learning is a subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data.
Deep Learning
Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the human brain, and they can be used to solve a wide variety of problems, including image recognition, natural language processing, and speech recognition.
Supervised Learning
Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns.
Unsupervised Learning
Unsupervised learning is a type of machine learning that learns from data without human supervision.
Reinforcement Learning
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal.
Generative AI
Generative AI is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
Few-Shot Learning
A type of machine learning where a model is trained to recognize new categories of data from only a few examples, in contrast to traditional methods that require large amounts of training data.
LLMs (Large Language Models)
A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive datasets to understand, summarize, generate, and predict new content.
Neural Network
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Transformer
Transformers are a type of neural network architecture that transforms or changes an input sequence into an output sequence. They do this by learning context and tracking relationships between sequence components.
Transfer Learning
A machine learning method where a model developed for a task is reused as the starting point for a model on a second task, often allowing for improved performance with less training data.
Embeddings
Embeddings are numerical representations of real-world objects that machine learning (ML) and artificial intelligence (AI) systems use to understand complex knowledge domains like humans do.
Vector Database
A vector database that allows you to store, index, and query vector embeddings, or numerical representations of unstructured data, such as text, images, or audio.
Token
Tokens are chunks of text that the model processes. They can be as short as one character or as long as one word (e.g., 'a' or 'apple'), and in some languages can be even longer than one word.
Natural Language Processing (NLP)
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language.
AGI (Artificial General Intelligence)
Artificial general intelligence (AGI) refers to the hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can. It is a type of artificial intelligence (AI) that aims to mimic the cognitive abilities of the human brain.
Computer Vision
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs — and take actions or make recommendations based on that information.
Inference
Inference is the process of running live data through a trained AI model to make a prediction or solve a task.
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
Prompt
A prompt is a natural language text that requests the generative AI to perform a specific task.
Federated Learning
A machine learning technique that trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them.
Edge AI
The deployment of AI algorithms and processing on edge devices (like smartphones or IoT devices) rather than in the cloud, allowing for faster processing and improved privacy.
Adversarial Machine Learning
Adversarial machine learning (AML) is the process of extracting information about the behavior and characteristics of an ML system and/or learning how to manipulate the inputs into an ML system in order to obtain a preferred outcome.
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